Acute Complication Prediction and Diagnosis Model CLSTM-BPR:A Fusion Method of Time Series Deep Learning and Bayesian Personalized Ranking

作     者：Xi Chen Quan Cheng 

作者机构：School of Economics and ManagementFuzhou UniversityFuzhou 350108China 

出 版 物：《Tsinghua Science and Technology》 (清华大学学报自然科学版（英文版）)

年 卷 期：2024年第29卷第5期

页      面：1509-1523页

核心收录：

学科分类：1002[医学-临床医学] 100201[医学-内科学(含：心血管病、血液病、呼吸系病、消化系病、内分泌与代谢病、肾病、风湿病、传染病)] 10[医学] 

基　　金：supported by the Social Science Fund of China(No.19BTQ072) 

主　　题：Long Short-Term Memory(LSTM) Bayesian Personalized Ranking(BPR) sudden illnesses disease predictions 

摘      要：Acute complication prediction model is of great importance for the overall reduction of premature death in chronic *** CLSTM-BPR proposed in this paper aims to improve the accuracy,interpretability,and generalizability of the existing disease prediction ***,through its complex neural network structure,CLSTM-BPR considers both disease commonality and patient characteristics in the prediction ***,by splicing the time series prediction algorithm and classifier,the judgment basis is given along with the prediction ***,this model introduces the pairwise algorithm Bayesian Personalized Ranking(BPR)into the medical field for the first time,and achieves a good result in the diagnosis of six acute *** on the Medical Information Mart for Intensive Care IV(MIMIC-IV)dataset show that the average Mean Absolute Error(MAE)of biomarker value prediction of the CLSTM-BPR model is 0.26,and the average accuracy(ACC)of the CLSTM-BPR model for acute complication diagnosis is 92.5%.Comparison experiments and ablation experiments further demonstrate the reliability of CLSTM-BPR in the prediction of acute complication,which is an advancement of current disease prediction tools.